Toolkit Definitions

Data Types & Levels

Community‑level data — Views and experiences shared directly by community members (e.g., ongoing barriers to employment described by residents).

Service‑level data — Information about clients, activities, and performance within programs or agencies (e.g., number of youth enrolled in a program).

Population‑level data — Data describing the entire population in a geographic area, larger than any single service system (e.g., local unemployment rate).

Quantitative data — Information recorded in standardized units that support counting, measuring, or ordered comparison, such as counts, rates, proportions, scales, time trends, cross-tabulations, correlations, etc. They provide a consistent way to track change over time, compare results across populations, and benchmark against regional or national figures. 

Qualitative data — Information captured as words, narratives, observations, or images that illuminate meaning, context, and experience. Provide the nuance that complements and explains what the numbers show. These may be collected via interviews, sharing circles, focus groups, observation, open-ended survey responses, storytelling, etc.

Aggregate data — Data combined into groups or totals.

Disaggregate data — Data broken down by categories such as age, gender, income, identity, or neighbourhood, when safe and appropriate.

Mixed Methods — Using both quantitative and qualitative data to provide a fuller understanding of trends, context, and lived experience.

 

Data Collection & Data Sources

Sampling — The approach used to decide who or what will be included in data collection. Sampling methods can be purposeful (e.g., speaking with specific groups or communities), convenience based (e.g., those easiest to reach, or via connections using “snowball” approaches), or random, depending on the goals, context, and equity considerations of the project.

Honoraria — Money, gift-cards or other items of value offered to recognize a person’s time, knowledge, and contributions during evaluation or engagement activities. Honoraria are a form of reciprocity and should reflect local expectations and guidance from the community involved.

Administrative data — A common type of service-level data; routinely collected as part of delivering services or programs (e.g., intake forms, case notes, service usage records) that can be used for M&E.

Surveys — Structured questionnaires used to collect standardized information from residents, clients, or partners, often through online, phone, or in‑person formats.

Interviews — Conversations used to gather in‑depth qualitative insights from individuals or small groups (e.g., 1–3 people from a similar role or organization), using guided or semi‑structured questions.

Focus groups — Facilitated discussions with small groups to explore shared experiences, priorities, or perspectives on a topic.

Sharing circles — A relational, culturally grounded approach, traditionally used in many Indigenous communities, that brings participants together in a respectful circle format to share experiences, insights, and teachings. Sharing circles intentionally disrupt Western power dynamics in research, monitoring, or evaluation by having everyone sit together in a way that emphasizes equality, respect, and collective voice.

Community engagement — Information drawn from community conversations, open houses, or other participatory methods.

Third-party or public datasets — Data compiled and released by organizations such as Statistics Canada, Public Safety Canada, or provincial ministries (e.g., Census, police-reported crime, health indicators).

Observation — Direct watching or documenting of activities, environments, or interactions (e.g., safety audits, program observations).

Document review — Using existing materials such as reports, policies, meeting minutes, or case notes as data sources for analysis.

Environmental scans — Systematic reviews of contextual information such as demographic trends, housing markets, or service availability.

Literature review — A structured process of collecting and synthesizing research, reports, and existing evidence to inform context, practice, or evaluation design.

Digital or sensor data — Information collected through automated systems (e.g., foot traffic counters, incident reporting apps).

 

Data Quality & Management

Data collection — Gathering information using surveys, interviews, administrative data, or other methods.

Data entry — Inputting data into a database or system.

Data integrity — Ensuring data is accurate, complete, and consistent.

Data cleaning — Identifying and correcting errors, missing values, or inconsistencies.

Data dictionary — A reference source that defines each data element to support consistency across partners and over time.

Quality assurance — Procedures put in place before and during data collection to prevent errors and support consistent processes.

Quality control — Activities that occur during or after data collection to detect and correct errors and maintain accuracy.

Data stewardship — Responsible management of data across its lifecycle, including privacy, security, quality, and ethical use.

• Data steward or champion — Individual(s) responsible for leading and supporting data stewardship within an organization or partnership.

Data governance — The policies, processes, and roles that guide how data is managed, accessed, protected, and used across partners.

Data access — Permissions that determine who can view, use, or download specific datasets or results.

Data analysis — The process of examining and organizing data to identify patterns, trends, and insights that help answer monitoring or evaluation questions.

• Coding: Sorting qualitative information (e.g., quotes, notes, comments) into categories or themes so that patterns can be more easily identified and compared.
• Triangulation: Using multiple data sources or methods to enrich interpretation and avoid misrepresentation.
• Sense‑making: A collaborative process where partners interpret data together, bringing local knowledge and context to ensure findings are accurate, meaningful, and grounded in real‑world experience.

Data communication — The practice of sharing findings clearly and responsibly using formats such as reports, briefs, articles, stories, presentations, etc. Effective data communication tailors messages to different audiences and helps ensure insights are understood, trusted, and used for decision‑making.

Data visualization — The process of presenting data using charts, maps, dashboards, infographics, etc. to make patterns, trends, and insights easier to understand. Good visualization highlights what matters, supports accessibility, and helps audiences quickly grasp key messages.

 

Privacy, Ethics & Indigenous Data Governance

Positionality — An acknowledgement of how a person’s background, identity, values, and experiences shape their perspectives, relationships, and interpretations in research, evaluation or engagement. Reflecting on positionality helps promote cultural humility, transparency, and awareness of power dynamics when working with communities.

Consent — An ongoing process that ensures participants understand how their information will be used and can revise or withdraw permissions at any time.

Privacy & confidentiality — Duties to safeguard information, limit identifiability, and manage data sharing or linkage appropriately.

Tri‑Council Policy Statement on Research Ethics, Version 2 (TCPS 2) — A Canadian research ethics policy that sets out principles and guidance for conducting ethical research involving humans. It includes specific guidance for working respectfully with First Nations, Inuit, and Métis Peoples (Chapter 9), emphasizing consent, reciprocity, community engagement, and cultural safety.

OCAP® Principles — Ownership, Control, Access, and Possession: foundational principles affirming First Nations data sovereignty from the First Nations Information Governance Centre.

 

CSWB‑Specific Terms

Community assessment — Gathering information with partners and residents to understand local safety conditions, strengths, and concerns as the first stage of a CSWB planning process.

Risk & protective factors — Conditions that increase risk (e.g., instability, discrimination) or enhance safety and well‑being (e.g., connection, housing stability).

Collective impact — An approach where multiple partners/sectors commit to a common agenda, shared measurement, and coordinated action to address complex issues.

Equity‑deserving groups — Communities who face structural barriers or discrimination and benefit from targeted approaches to achieve fair outcomes.

Systems change — Shifts in structures, policies, practices, or relationships that sustain long‑term improvements in outcomes of interest.

Top-down approach — Processes led by individuals or organizations in positions of authority, such as government officials, system leaders, or researchers. These approaches often emphasize structure, consistency, and quantitative evidence, which can support rigour and comparability, but may overlook local context, community priorities, or lived experience.

Bottom-up approach — Processes that emphasize participatory engagement with community members, service providers, and local groups to cocreate inputs that reflect local realities and priorities. These approaches can generate rich, context sensitive insights and stronger community ownership, though findings may be less standardized or less generalizable.